Using a Variable-Friction Robot Hand to Determine Proprioceptive Features for Object Classification During Within-Hand-Manipulation

2020

Article

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Interactions with an object during within-hand manipulation (WIHM) constitutes an assortment of gripping, sliding, and pivoting actions. In addition to manipulation benefits, the re-orientation and motion of the objects within-the-hand also provides a rich array of additional haptic information via the interactions to the sensory organs of the hand. In this article, we utilize variable friction (VF) robotic fingers to execute a rolling WIHM on a variety of objects, while recording "proprioceptive" actuator data, which is then used for object classification (i.e., without tactile sensors). Rather than hand-picking a select group of features for this task, our approach begins with 66 general features, which are computed from actuator position and load profiles for each object-rolling manipulation, based on gradient changes. An Extra Trees classifier performs object classification while also ranking each feature's importance. Using only the six most-important "Key Features" from the general set, a classification accuracy of 86% was achieved for distinguishing the six geometric objects included in our data set. Comparatively, when all 66 features are used, the accuracy is 89.8%.

@article{Spiers20-TH-Variable,
  title = {Using a Variable-Friction Robot Hand to Determine Proprioceptive Features for Object Classification During Within-Hand-Manipulation},
  author = {Spiers, Adam J. and Morgan, Andrew S. and Srinivasan, Krishnan and Calli, Berk and Dollar, Aaron M.},
  journal = {IEEE Transactions on Haptics},
  volume = {13},
  number = {3},
  pages = {600--610},
  month = jul,
  year = {2020},
  doi = {10.1109/TOH.2019.2958669},
  month_numeric = {7}
}